Update process_interview.py

process_interview.py

@@ -25,10 +25,9 @@ from reportlab.lib.units import inch
 from reportlab.lib import colors
 import matplotlib.pyplot as plt
 import matplotlib
-
-matplotlib.use('Agg')  # --- FIX: تحديد backend لـ matplotlib ---
+matplotlib.use('Agg') # --- FIX: تحديد backend لـ matplotlib ---
 from reportlab.platypus import Image
-import io  # --- FIX: إضافة import io لـ BytesIO ---
+import io # --- FIX: إضافة import io لـ BytesIO ---
 # --- End Imports for enhanced PDF ---
 from transformers import AutoTokenizer, AutoModel
 import spacy
@@ -216,14 +215,14 @@ def process_utterance(utterance, full_audio, wav_file):
         segment.export(temp_path, format="wav")
 
         with torch.no_grad():
-            embedding = speaker_model.get_embedding(temp_path).cpu().numpy()  # Ensure numpy array
-
+            embedding = speaker_model.get_embedding(temp_path).cpu().numpy() # Ensure numpy array
+        
         # --- FIX: Convert embedding to a flat list for Pinecone query ---
         embedding_list = embedding.flatten().tolist()
         # --- End FIX ---
 
         query_result = index.query(
-            vector=embedding_list,  # Use the corrected flat list
+            vector=embedding_list, # Use the corrected flat list
             top_k=1,
             include_metadata=True
         )
@@ -234,7 +233,7 @@ def process_utterance(utterance, full_audio, wav_file):
         else:
             speaker_id = f"unknown_{uuid.uuid4().hex[:6]}"
             speaker_name = f"Speaker_{speaker_id[-4:]}"
-            index.upsert([(speaker_id, embedding_list, {"speaker_name": speaker_name})])  # Use corrected list
+            index.upsert([(speaker_id, embedding_list, {"speaker_name": speaker_name})]) # Use corrected list
 
         os.remove(temp_path)
 
@@ -242,7 +241,7 @@ def process_utterance(utterance, full_audio, wav_file):
             **utterance,
             'speaker': speaker_name,
             'speaker_id': speaker_id,
-            'embedding': embedding_list  # Store the corrected list
+            'embedding': embedding_list # Store the corrected list
         }
     except Exception as e:
         logger.error(f"Utterance processing failed: {str(e)}", exc_info=True)
@@ -580,9 +579,9 @@ def calculate_acceptance_probability(analysis_data: Dict) -> float:
     # Normalize to 0-1 and then to percentage
     # These max/min values are rough estimates and should be calibrated with real data
     min_possible_score = (0 * w_confidence) + (0 * abs(w_anxiety)) + (0 * w_fluency) + (0 * w_speaking_rate) + (
-            0 * abs(w_filler_repetition)) + (0 * w_content_strengths)
+                0 * abs(w_filler_repetition)) + (0 * w_content_strengths)
     max_possible_score = (1 * w_confidence) + (1 * abs(w_anxiety)) + (1 * w_fluency) + (1 * w_speaking_rate) + (
-            1 * abs(w_filler_repetition)) + (1 * w_content_strengths)
+                1 * abs(w_filler_repetition)) + (1 * w_content_strengths)
 
     # Prevent division by zero if all weights are zero or min/max are same
     if max_possible_score == min_possible_score:
@@ -787,13 +786,12 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                 # It relies on matplotlib and Image (from reportlab.platypus)
                 generate_anxiety_confidence_chart(voice_analysis['composite_scores'], chart_path)
                 if os.path.exists(chart_path):
-                    img = Image(chart_path, width=3.5 * inch, height=2.0 * inch)
+                    img = Image(chart_path, width=3.5*inch, height=2.0*inch)
                     story.append(img)
                     story.append(Spacer(1, 0.1 * inch))
                     os.remove(chart_path)
-            except NameError:  # Catch NameError if function is truly not defined
-                logger.warning(
-                    "Chart generation function 'generate_anxiety_confidence_chart' is not defined. Skipping chart.")
+            except NameError: # Catch NameError if function is truly not defined
+                logger.warning("Chart generation function 'generate_anxiety_confidence_chart' is not defined. Skipping chart.")
             except Exception as chart_e:
                 logger.warning(f"Could not add chart image to PDF: {chart_e}. Please check matplotlib installation.")
             # --- End FIX ---
@@ -823,7 +821,7 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                 if line.strip():
                     # Handle bullet points from Gemini
                     if line.strip().startswith('-'):
-                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style))  # Remove the '-' and strip
+                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style)) # Remove the '-' and strip
                     else:
                         story.append(Paragraph(line.strip(), body_text))
             story.append(Spacer(1, 0.2 * inch))
@@ -846,7 +844,7 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                 if line.strip():
                     # Handle bullet points from Gemini
                     if line.strip().startswith('-'):
-                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style))  # Remove the '-' and strip
+                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style)) # Remove the '-' and strip
                     else:
                         story.append(Paragraph(line.strip(), body_text))
             story.append(Spacer(1, 0.2 * inch))